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BC render-3d-character-explainer

Assemble an animated character-explainer video ad (~77s, 9:16, any art style: Pixar-style 3D, claymation, anime, paper-cut, felt) on an "N types of X" listicle spine: one recurring human protagonist plus one persona character per list item. Given per-scene clips, a scene timing table and narration, it trims, normalises, concatenates and mixes them, reusing the source ad's audio (restyle) or fresh per-scene voiceover over a music bed (original), loops a scene's keyframe when its clip failed, and burns captions last. Free, deterministic assembly (Python and ffmpeg, no paid calls); the recipe supplies the clips, keyframes, audio and captions. Use for the 3d-character-explainer format.

athina-ai/goose-skills Agent Skills author: athina-ai 6 files · 2 scripts body ≈ 2 050 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Assemble an animated character-explainer video ad (~77s, 9:16, any art style: Pixar-style 3D, claymation, anime, paper-cut, felt) on an "N types of X"…

As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, running it twice

GeneratorSoftware developmentMedia and videotype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
82
Run on models
none yet
Process rating
C
56/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: render-3d-character-explainer (gooseworks-ai/goose-skills)

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 5. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "status"

    Process rating: all ten parameters 56/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 6 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 22 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2050 tokens

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 690: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 22 items
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.